Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

Numeric10
Categorical1

Warnings

target is uniformly distributed Uniform
X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2021-02-12 17:24:32.088724
Analysis finished2021-02-12 17:37:46.033254
Duration13 minutes and 13.94 seconds
Software versionpandas-profiling v2.10.1
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01332298912
Minimum-2.817856632
Maximum3.391679716
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:37:55.873914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.817856632
5-th percentile-1.671570117
Q1-0.6492916173
median0.007395975759
Q30.6802459138
95-th percentile1.641895087
Maximum3.391679716
Range6.209536348
Interquartile range (IQR)1.329537531

Descriptive statistics

Standard deviation0.9966387317
Coefficient of variation (CV)74.80594054
Kurtosis-0.01752818391
Mean0.01332298912
Median Absolute Deviation (MAD)0.6683274119
Skewness-0.007347721206
Sum13.32298912
Variance0.9932887614
MonotocityNot monotonic
2021-02-12T12:38:04.467140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1287960841
 
0.1%
1.3608684121
 
0.1%
0.86776271731
 
0.1%
-0.25772257681
 
0.1%
0.43854999641
 
0.1%
0.25819394971
 
0.1%
0.58075464811
 
0.1%
-1.6538630961
 
0.1%
-1.1050077991
 
0.1%
0.38906390191
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.8178566321
0.1%
-2.8076683671
0.1%
-2.7247537051
0.1%
-2.5677878361
0.1%
-2.5412764081
0.1%
-2.5394815231
0.1%
-2.4943207751
0.1%
-2.468602191
0.1%
-2.2892924751
0.1%
-2.2810544071
0.1%
ValueCountFrequency (%)
3.3916797161
0.1%
3.0829503581
0.1%
2.8234992661
0.1%
2.7242892991
0.1%
2.7051738411
0.1%
2.6384570821
0.1%
2.6301247881
0.1%
2.5860489351
0.1%
2.5812963731
0.1%
2.5025614921
0.1%

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.08215615175
Minimum-3.091623743
Maximum2.985950602
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:38:13.037395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.091623743
5-th percentile-1.720820602
Q1-0.730202631
median-0.09968350913
Q30.5773048137
95-th percentile1.488118524
Maximum2.985950602
Range6.077574345
Interquartile range (IQR)1.307507445

Descriptive statistics

Standard deviation0.9774283276
Coefficient of variation (CV)-11.89720194
Kurtosis0.02693400397
Mean-0.08215615175
Median Absolute Deviation (MAD)0.6554975989
Skewness-0.06472709181
Sum-82.15615175
Variance0.9553661355
MonotocityNot monotonic
2021-02-12T12:38:21.784960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.0502266581
 
0.1%
0.95877012291
 
0.1%
0.66850209411
 
0.1%
-0.014227133471
 
0.1%
-1.0152837841
 
0.1%
0.41311916391
 
0.1%
-0.48852569421
 
0.1%
0.43052248561
 
0.1%
-0.14155759561
 
0.1%
-1.2425046141
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0916237431
0.1%
-2.9394047881
0.1%
-2.8743288261
0.1%
-2.8535898191
0.1%
-2.7914779671
0.1%
-2.7852582141
0.1%
-2.717538421
0.1%
-2.569069641
0.1%
-2.5523156011
0.1%
-2.5294345461
0.1%
ValueCountFrequency (%)
2.9859506021
0.1%
2.6464263941
0.1%
2.6153020781
0.1%
2.3685962881
0.1%
2.3398660381
0.1%
2.3027388031
0.1%
2.179723491
0.1%
2.1779296321
0.1%
2.1314769891
0.1%
2.0905249611
0.1%

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01637452131
Minimum-2.890401503
Maximum3.17641795
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:38:31.058111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.890401503
5-th percentile-1.625723008
Q1-0.6518343415
median0.03143032501
Q30.6673254028
95-th percentile1.656828113
Maximum3.17641795
Range6.066819453
Interquartile range (IQR)1.319159744

Descriptive statistics

Standard deviation0.9895479683
Coefficient of variation (CV)60.43217687
Kurtosis-0.05741177719
Mean0.01637452131
Median Absolute Deviation (MAD)0.6635107059
Skewness-0.007480272672
Sum16.37452131
Variance0.9792051816
MonotocityNot monotonic
2021-02-12T12:38:40.379340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.71811654171
 
0.1%
1.5110700661
 
0.1%
-0.54416021741
 
0.1%
1.0143456861
 
0.1%
2.0534310551
 
0.1%
0.61455071641
 
0.1%
0.21283771911
 
0.1%
1.1495221561
 
0.1%
1.1140027471
 
0.1%
0.61885243281
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.8904015031
0.1%
-2.8470218921
0.1%
-2.7802779471
0.1%
-2.4320035271
0.1%
-2.3886730061
0.1%
-2.3421334321
0.1%
-2.3171485551
0.1%
-2.3125559741
0.1%
-2.2765026821
0.1%
-2.2451083851
0.1%
ValueCountFrequency (%)
3.176417951
0.1%
2.9116371611
0.1%
2.7579818231
0.1%
2.7532869081
0.1%
2.7511952461
0.1%
2.7159183231
0.1%
2.5434220661
0.1%
2.4374742621
0.1%
2.4169393321
0.1%
2.3722291571
0.1%

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05424440286
Minimum-3.799011716
Maximum2.65378911
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:38:49.352431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.799011716
5-th percentile-1.5331302
Q1-0.6017712333
median0.04562746503
Q30.7302150817
95-th percentile1.655529459
Maximum2.65378911
Range6.452800826
Interquartile range (IQR)1.331986315

Descriptive statistics

Standard deviation0.9743913548
Coefficient of variation (CV)17.96298426
Kurtosis0.06071029772
Mean0.05424440286
Median Absolute Deviation (MAD)0.6709215925
Skewness-0.1396745838
Sum54.24440286
Variance0.9494385124
MonotocityNot monotonic
2021-02-12T12:38:58.521771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32787130181
 
0.1%
0.70924402911
 
0.1%
-0.1983491581
 
0.1%
0.51008107251
 
0.1%
0.73738241911
 
0.1%
-1.0531925311
 
0.1%
-0.6854695361
 
0.1%
-0.33249538571
 
0.1%
0.59858545511
 
0.1%
-0.30377241991
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.7990117161
0.1%
-3.2106486421
0.1%
-2.7734688281
0.1%
-2.6157886271
0.1%
-2.6111544041
0.1%
-2.5439061041
0.1%
-2.4834136781
0.1%
-2.4256435581
0.1%
-2.421399141
0.1%
-2.3983658311
0.1%
ValueCountFrequency (%)
2.653789111
0.1%
2.5955946821
0.1%
2.584954041
0.1%
2.5233621851
0.1%
2.4925199911
0.1%
2.4139485371
0.1%
2.354670891
0.1%
2.3028205561
0.1%
2.29207621
0.1%
2.2354351551
0.1%

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01127119572
Minimum-3.120168417
Maximum3.565280655
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:39:06.921455image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.120168417
5-th percentile-1.627923522
Q1-0.691949217
median0.00107417604
Q30.6807467696
95-th percentile1.640082052
Maximum3.565280655
Range6.685449071
Interquartile range (IQR)1.372695987

Descriptive statistics

Standard deviation0.9907749356
Coefficient of variation (CV)87.90326782
Kurtosis-0.1014112414
Mean0.01127119572
Median Absolute Deviation (MAD)0.6909642382
Skewness0.06755037184
Sum11.27119572
Variance0.9816349731
MonotocityNot monotonic
2021-02-12T12:39:15.171297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.79276096351
 
0.1%
0.45307331391
 
0.1%
1.0236097621
 
0.1%
-1.402585461
 
0.1%
0.078134443981
 
0.1%
0.5719337871
 
0.1%
0.66344595451
 
0.1%
-0.90598474731
 
0.1%
-0.44052598431
 
0.1%
0.49967113461
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.1201684171
0.1%
-2.5396951251
0.1%
-2.5347831111
0.1%
-2.5000117021
0.1%
-2.3556894911
0.1%
-2.33789821
0.1%
-2.328741021
0.1%
-2.2999853951
0.1%
-2.2518536831
0.1%
-2.2115666621
0.1%
ValueCountFrequency (%)
3.5652806551
0.1%
3.3489397031
0.1%
2.8853154411
0.1%
2.5666524761
0.1%
2.5599018331
0.1%
2.4015345741
0.1%
2.3799647371
0.1%
2.3729205081
0.1%
2.3177889591
0.1%
2.3078268031
0.1%

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004997230854
Minimum-3.076220986
Maximum3.50531306
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:39:23.653943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.076220986
5-th percentile-1.57559908
Q1-0.6729406887
median-0.0340290857
Q30.6398759511
95-th percentile1.766392547
Maximum3.50531306
Range6.581534047
Interquartile range (IQR)1.31281664

Descriptive statistics

Standard deviation0.9918672203
Coefficient of variation (CV)-198.48337
Kurtosis0.00999330766
Mean-0.004997230854
Median Absolute Deviation (MAD)0.6596294524
Skewness0.1507639801
Sum-4.997230854
Variance0.9838005828
MonotocityNot monotonic
2021-02-12T12:39:32.797195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.31828858351
 
0.1%
1.0183824241
 
0.1%
1.4902518681
 
0.1%
-2.074621061
 
0.1%
-0.3406216641
 
0.1%
0.038550553741
 
0.1%
0.06922067821
 
0.1%
0.58395924771
 
0.1%
0.40987987881
 
0.1%
0.54354329011
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.0762209861
0.1%
-2.733815321
0.1%
-2.6674163461
0.1%
-2.5040170621
0.1%
-2.484050971
0.1%
-2.4181027461
0.1%
-2.3718841661
0.1%
-2.3598157531
0.1%
-2.3304978011
0.1%
-2.3164193991
0.1%
ValueCountFrequency (%)
3.505313061
0.1%
2.8024142441
0.1%
2.7452124291
0.1%
2.7430250221
0.1%
2.6575840431
0.1%
2.6514860571
0.1%
2.5655495841
0.1%
2.5391147271
0.1%
2.4035231521
0.1%
2.4004833221
0.1%

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01725883686
Minimum-2.896430275
Maximum3.266842797
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:39:41.159135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.896430275
5-th percentile-1.566259867
Q1-0.6416443216
median-0.002292700517
Q30.6446287086
95-th percentile1.715537999
Maximum3.266842797
Range6.163273072
Interquartile range (IQR)1.28627303

Descriptive statistics

Standard deviation0.9798349171
Coefficient of variation (CV)56.77294045
Kurtosis-0.02895274187
Mean0.01725883686
Median Absolute Deviation (MAD)0.6415478236
Skewness0.06235598254
Sum17.25883686
Variance0.9600764647
MonotocityNot monotonic
2021-02-12T12:39:49.664130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.56794705781
 
0.1%
0.88272577311
 
0.1%
0.33730227221
 
0.1%
-0.56638197931
 
0.1%
1.4759078661
 
0.1%
-0.072320298861
 
0.1%
0.10955437711
 
0.1%
1.2102997621
 
0.1%
-0.18243064521
 
0.1%
1.6519547591
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.8964302751
0.1%
-2.8881706731
0.1%
-2.6734242821
0.1%
-2.6201578831
0.1%
-2.5572571691
0.1%
-2.4035662911
0.1%
-2.37353211
0.1%
-2.3038539251
0.1%
-2.2943764741
0.1%
-2.2874449371
0.1%
ValueCountFrequency (%)
3.2668427971
0.1%
3.2609753871
0.1%
2.7137472951
0.1%
2.5728614971
0.1%
2.4416016911
0.1%
2.3899961611
0.1%
2.3676254821
0.1%
2.3403986981
0.1%
2.2891454931
0.1%
2.2801344541
0.1%

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00315437226
Minimum-3.292884893
Maximum2.879456362
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:39:58.031250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-3.292884893
5-th percentile-1.643699834
Q1-0.6136942
median0.01422918699
Q30.649604114
95-th percentile1.593021613
Maximum2.879456362
Range6.172341255
Interquartile range (IQR)1.263298314

Descriptive statistics

Standard deviation0.9933019936
Coefficient of variation (CV)-314.8968833
Kurtosis0.2035366473
Mean-0.00315437226
Median Absolute Deviation (MAD)0.6342811117
Skewness-0.1614814406
Sum-3.15437226
Variance0.9866488505
MonotocityNot monotonic
2021-02-12T12:40:06.413045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1917341591
 
0.1%
0.37326142311
 
0.1%
0.48440514661
 
0.1%
0.25984888961
 
0.1%
0.041497322091
 
0.1%
-1.4564372991
 
0.1%
0.93934676131
 
0.1%
-0.77249283371
 
0.1%
0.85103221111
 
0.1%
-0.27337854591
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.2928848931
0.1%
-3.2068385461
0.1%
-3.0857010161
0.1%
-2.9483691981
0.1%
-2.9040304091
0.1%
-2.6749010991
0.1%
-2.6483572791
0.1%
-2.5935001931
0.1%
-2.5930986131
0.1%
-2.5430568571
0.1%
ValueCountFrequency (%)
2.8794563621
0.1%
2.8417589351
0.1%
2.714541851
0.1%
2.6677093921
0.1%
2.5250551571
0.1%
2.432321091
0.1%
2.424787741
0.1%
2.3555632031
0.1%
2.3464815321
0.1%
2.3311654081
0.1%

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06174415758
Minimum-2.746367985
Maximum2.717859714
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:40:15.640999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.746367985
5-th percentile-1.527596654
Q1-0.5710526253
median0.01341169576
Q30.7393884486
95-th percentile1.665383116
Maximum2.717859714
Range5.464227699
Interquartile range (IQR)1.310441074

Descriptive statistics

Standard deviation0.9702933833
Coefficient of variation (CV)15.71474001
Kurtosis-0.1903006292
Mean0.06174415758
Median Absolute Deviation (MAD)0.66080917
Skewness-0.02358289755
Sum61.74415758
Variance0.9414692497
MonotocityNot monotonic
2021-02-12T12:40:24.121583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.042560622641
 
0.1%
0.44808992971
 
0.1%
0.52184651741
 
0.1%
0.93208643311
 
0.1%
-1.4082316441
 
0.1%
-0.19354301521
 
0.1%
0.4301136311
 
0.1%
-1.0687296891
 
0.1%
-0.63003708311
 
0.1%
0.62148596941
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.7463679851
0.1%
-2.7391854771
0.1%
-2.7166271851
0.1%
-2.6772658281
0.1%
-2.648854631
0.1%
-2.625624851
0.1%
-2.4129764181
0.1%
-2.355278381
0.1%
-2.3123372021
0.1%
-2.2332776641
0.1%
ValueCountFrequency (%)
2.7178597141
0.1%
2.7137738051
0.1%
2.680573861
0.1%
2.5664738731
0.1%
2.5143000981
0.1%
2.5037898451
0.1%
2.448820991
0.1%
2.2442794871
0.1%
2.2182039421
0.1%
2.1506274851
0.1%

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03995643188
Minimum-2.974348509
Maximum3.374071562
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-02-12T12:40:32.770237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.974348509
5-th percentile-1.598049329
Q1-0.7246269233
median-0.04676847601
Q30.6044159952
95-th percentile1.508172352
Maximum3.374071562
Range6.348420071
Interquartile range (IQR)1.329042918

Descriptive statistics

Standard deviation0.9727416258
Coefficient of variation (CV)-24.34505735
Kurtosis-0.03846159704
Mean-0.03995643188
Median Absolute Deviation (MAD)0.6687367747
Skewness0.08663439132
Sum-39.95643188
Variance0.9462262705
MonotocityNot monotonic
2021-02-12T12:40:41.159840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1661786061
 
0.1%
1.224444721
 
0.1%
0.34067776531
 
0.1%
0.4198833851
 
0.1%
-0.24214421841
 
0.1%
0.15183759381
 
0.1%
-0.80295219351
 
0.1%
-0.48122287891
 
0.1%
0.52150507621
 
0.1%
0.18551088791
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.9743485091
0.1%
-2.8491971251
0.1%
-2.8189754261
0.1%
-2.6581209011
0.1%
-2.6409303951
0.1%
-2.4468647641
0.1%
-2.4035148181
0.1%
-2.3272049671
0.1%
-2.3142678281
0.1%
-2.1878512911
0.1%
ValueCountFrequency (%)
3.3740715621
0.1%
2.8261431741
0.1%
2.7437685941
0.1%
2.6333501021
0.1%
2.5670550021
0.1%
2.4674756841
0.1%
2.4155224931
0.1%
2.3956517681
0.1%
2.3754847251
0.1%
2.3673726141
0.1%

target
Categorical

UNIFORM

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
500 
1
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0500
50.0%
1500
50.0%
2021-02-12T12:40:57.967471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-02-12T12:41:06.129405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring characters

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1000
100.0%

Most frequent character per category

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common1000
100.0%

Most frequent character per script

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1000
100.0%

Most frequent character per block

ValueCountFrequency (%)
1500
50.0%
0500
50.0%

Interactions

2021-02-12T12:24:41.936032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:24:50.440348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:24:58.737958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:25:07.766836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:25:16.729462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:25:26.018381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:25:35.270065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:25:43.590171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:25:52.514696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:26:01.318921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:26:09.785439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:26:17.946167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:26:26.610517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:26:34.871132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:26:43.710284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:26:52.754437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:27:01.286157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:27:10.379894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:27:19.059216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:27:28.222959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:27:37.170712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:27:45.910469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:27:54.423519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:28:03.099008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:28:11.558824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:28:20.169864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:28:28.842227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:28:37.434754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:28:45.845586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:28:54.153381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:29:03.530209image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:29:12.889934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:29:22.434489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:29:31.484350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:29:40.244056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:29:49.033994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:29:57.395142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:30:05.947245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:30:15.877128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:30:24.473993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:30:33.335441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:30:41.850579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:30:50.375753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:30:59.131687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:31:07.534341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:31:16.255231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:31:24.942682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:31:34.282829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:31:43.087002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:31:51.705638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:32:00.877706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:32:09.371327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:32:17.975276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:32:26.579411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:32:35.194607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:32:44.511493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:32:53.115201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:33:01.434072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:33:09.951402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:33:18.542143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:33:27.110148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:33:36.150295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:33:44.407204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:33:52.903892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:34:01.677928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:34:10.122085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:34:18.187226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:34:26.426044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:34:34.758149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:34:43.446320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:34:52.326939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:00.262025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:08.354472image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:16.190641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:24.419816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:33.266415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:41.207446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:49.058986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:35:57.339519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:36:05.302467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:36:13.307657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:36:21.546638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:36:29.953852image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:36:38.266106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:36:46.789905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:36:55.026255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:37:03.293312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:37:11.518344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:37:19.451939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-02-12T12:37:28.322456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-02-12T12:41:14.346677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-02-12T12:41:22.695085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-02-12T12:41:31.720006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-02-12T12:41:40.043309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-02-12T12:37:36.817077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-02-12T12:37:45.560808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

X0X1X2X3X4X5X6X7X8X9target
00.496553-0.503645-0.282967-1.0768110.724876-1.119351-1.577188-2.674901-0.8096260.1064541
10.2024401.0302601.6490300.6550550.4922171.2288151.9416960.2071560.5309880.0140201
20.594393-1.0081810.3605750.7092441.5512751.937625-1.806921-0.313360-1.145053-0.6947631
3-0.073275-1.763521-1.5844740.410860-0.5103200.139079-0.8777700.039895-0.1488391.2297290
4-0.035005-0.8916840.5352471.061094-1.8494620.3351120.6445331.3837660.664657-1.2268321
5-2.5412760.9254571.938646-0.3825791.859517-0.9365461.7152900.675109-0.000943-0.3671671
60.4616112.3027391.6732190.4657431.1926690.4940670.269591-0.578517-1.4750201.0551931
7-1.590878-0.0999511.292769-0.1426740.113031-0.5729531.1458941.067374-0.869665-0.6633180
8-1.183971-0.910872-1.0320781.163349-0.839303-0.469031-1.7215180.152640-0.962791-0.8097930
9-0.3679201.1209260.1411031.484343-0.372048-2.195175-0.2302451.2433421.3981710.8472950

Last rows

X0X1X2X3X4X5X6X7X8X9target
990-0.564115-0.1415580.798653-0.470239-0.2484260.511748-0.654984-0.0307280.307433-1.0252521
991-0.692745-0.932401-1.0428860.992772-0.0938870.193998-0.117766-0.1136150.878878-1.4603721
992-0.0342510.2835860.475541-0.3088460.0890060.9170560.881499-0.239186-0.9977550.3013351
9930.029375-0.4441200.693023-0.9735091.388686-0.0457361.234789-0.8120890.2373260.4346791
994-0.628896-1.423743-0.7905240.1182460.0752522.0709980.882726-0.027845-0.853027-1.6407120
9950.0439351.202035-0.300034-1.532361-0.3356630.071156-0.8832360.168363-0.164915-0.0617531
9960.610728-1.303592-0.233258-1.301738-0.0709240.575345-0.405644-1.0052750.632639-2.3272050
9970.017381-0.463505-2.0040830.307445-0.224617-1.407229-0.9087630.4355220.4873490.8761280
998-1.308519-1.613056-1.545084-0.502978-1.402585-0.5050950.6140391.1596550.807307-0.2147441
9990.768594-0.7577941.299329-1.657594-0.7871081.1329171.6672361.0397981.357423-0.5579120